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            <article class="content wrap" id="_content" data-uid="Keras.Applications.DenseNet.DenseNet201">
  
  
  <h1 id="Keras_Applications_DenseNet_DenseNet201" data-uid="Keras.Applications.DenseNet.DenseNet201" class="text-break">Class DenseNet201
  </h1>
  <div class="markdown level0 summary"><p>DenseNet models, with weights pre-trained on ImageNet.
This model and can be built both with 'channels_first' data format(channels, height, width) or 'channels_last' data format(height, width, channels).
The default input size for this model is 224x224.</p>
</div>
  <div class="markdown level0 conceptual"></div>
  <div class="inheritance">
    <h5>Inheritance</h5>
    <div class="level0"><span class="xref">System.Object</span></div>
    <div class="level1"><a class="xref" href="Keras.Keras.html">Keras</a></div>
    <div class="level2"><a class="xref" href="Keras.Base.html">Base</a></div>
    <div class="level3"><a class="xref" href="Keras.Models.BaseModel.html">BaseModel</a></div>
    <div class="level4"><a class="xref" href="Keras.Models.Model.html">Model</a></div>
    <div class="level5"><a class="xref" href="Keras.Applications.AppModelBase.html">AppModelBase</a></div>
    <div class="level6"><span class="xref">DenseNet201</span></div>
  </div>
  <div classs="implements">
    <h5>Implements</h5>
    <div><span class="xref">System.IDisposable</span></div>
  </div>
  <div class="inheritedMembers">
    <h5>Inherited Members</h5>
    <div>
      <a class="xref" href="Keras.Applications.AppModelBase.html#Keras_Applications_AppModelBase_DecodePredictions_Numpy_NDarray_System_Int32_">AppModelBase.DecodePredictions(NDarray, Int32)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Applications.AppModelBase.html#Keras_Applications_AppModelBase_PreprocessInput_Numpy_NDarray_System_String_">AppModelBase.PreprocessInput(NDarray, String)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Compile_Keras_StringOrInstance_System_String_System_String___System_Single___System_String_System_String___Numpy_NDarray___">BaseModel.Compile(StringOrInstance, String, String[], Single[], String, String[], NDarray[])</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Fit_Numpy_NDarray_Numpy_NDarray_System_Nullable_System_Int32__System_Int32_System_Int32_Keras_Callbacks_Callback___System_Single_Numpy_NDarray___System_Boolean_System_Collections_Generic_Dictionary_System_Int32_System_Single__Numpy_NDarray_System_Int32_System_Nullable_System_Int32__System_Nullable_System_Int32__">BaseModel.Fit(NDarray, NDarray, Nullable&lt;Int32&gt;, Int32, Int32, Callback[], Single, NDarray[], Boolean, Dictionary&lt;Int32, Single&gt;, NDarray, Int32, Nullable&lt;Int32&gt;, Nullable&lt;Int32&gt;)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Evaluate_Numpy_NDarray_Numpy_NDarray_System_Nullable_System_Int32__System_Int32_Numpy_NDarray_System_Nullable_System_Int32__Keras_Callbacks_Callback___">BaseModel.Evaluate(NDarray, NDarray, Nullable&lt;Int32&gt;, Int32, NDarray, Nullable&lt;Int32&gt;, Callback[])</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Predict_Numpy_NDarray_System_Nullable_System_Int32__System_Int32_System_Nullable_System_Int32__Keras_Callbacks_Callback___">BaseModel.Predict(NDarray, Nullable&lt;Int32&gt;, Int32, Nullable&lt;Int32&gt;, Callback[])</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_TrainOnBatch_Numpy_NDarray_Numpy_NDarray_Numpy_NDarray_System_Collections_Generic_Dictionary_System_Int32_System_Single__">BaseModel.TrainOnBatch(NDarray, NDarray, NDarray, Dictionary&lt;Int32, Single&gt;)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_TestOnBatch_Numpy_NDarray_Numpy_NDarray_Numpy_NDarray_">BaseModel.TestOnBatch(NDarray, NDarray, NDarray)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_PredictOnBatch_Numpy_NDarray_">BaseModel.PredictOnBatch(NDarray)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_ToJson">BaseModel.ToJson()</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_SaveWeight_System_String_">BaseModel.SaveWeight(String)</a>
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      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Save_System_String_">BaseModel.Save(String)</a>
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    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_LoadWeight_System_String_">BaseModel.LoadWeight(String)</a>
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      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_LoadModel_System_String_">BaseModel.LoadModel(String)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_ModelFromJson_System_String_">BaseModel.ModelFromJson(String)</a>
    </div>
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      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_ModelFromYaml_System_String_">BaseModel.ModelFromYaml(String)</a>
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      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_SaveOnnx_System_String_">BaseModel.SaveOnnx(String)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_Summary_System_Nullable_System_Int32__System_Single___">BaseModel.Summary(Nullable&lt;Int32&gt;, Single[])</a>
    </div>
    <div>
      <a class="xref" href="Keras.Models.BaseModel.html#Keras_Models_BaseModel_SaveTensorflowJSFormat_System_String_System_Boolean_">BaseModel.SaveTensorflowJSFormat(String, Boolean)</a>
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      <a class="xref" href="Keras.Base.html#Keras_Base_None">Base.None</a>
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      <a class="xref" href="Keras.Base.html#Keras_Base_Init">Base.Init()</a>
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    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_ToPython">Base.ToPython()</a>
    </div>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_InvokeStaticMethod_System_Object_System_String_System_Collections_Generic_Dictionary_System_String_System_Object__">Base.InvokeStaticMethod(Object, String, Dictionary&lt;String, Object&gt;)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_InvokeMethod_System_String_System_Collections_Generic_Dictionary_System_String_System_Object__">Base.InvokeMethod(String, Dictionary&lt;String, Object&gt;)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_Item_System_String_">Base.Item[String]</a>
    </div>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_Instance">Keras.Instance</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_keras">Keras.keras</a>
    </div>
    <div>
      <a class="xref" href="Keras.Keras.html#Keras_Keras_keras2onnx">Keras.keras2onnx</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_tfjs">Keras.tfjs</a>
    </div>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_Dispose">Keras.Dispose()</a>
    </div>
    <div>
      <a class="xref" href="Keras.Keras.html#Keras_Keras_ToTuple_System_Array_">Keras.ToTuple(Array)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Keras.html#Keras_Keras_ToList_System_Array_">Keras.ToList(Array)</a>
    </div>
    <div>
      <span class="xref">System.Object.Equals(System.Object)</span>
    </div>
    <div>
      <span class="xref">System.Object.Equals(System.Object, System.Object)</span>
    </div>
    <div>
      <span class="xref">System.Object.GetHashCode()</span>
    </div>
    <div>
      <span class="xref">System.Object.GetType()</span>
    </div>
    <div>
      <span class="xref">System.Object.MemberwiseClone()</span>
    </div>
    <div>
      <span class="xref">System.Object.ReferenceEquals(System.Object, System.Object)</span>
    </div>
    <div>
      <span class="xref">System.Object.ToString()</span>
    </div>
  </div>
  <h6><strong>Namespace</strong>: <a class="xref" href="Keras.Applications.DenseNet.html">Keras.Applications.DenseNet</a></h6>
  <h6><strong>Assembly</strong>: Keras.dll</h6>
  <h5 id="Keras_Applications_DenseNet_DenseNet201_syntax">Syntax</h5>
  <div class="codewrapper">
    <pre><code class="lang-csharp hljs">public class DenseNet201 : AppModelBase, IDisposable</code></pre>
  </div>
  <h3 id="constructors">Constructors
  </h3>
  <span class="small pull-right mobile-hide">
    <span class="divider">|</span>
    <a href="https://github.com/SciSharp/Keras.NET/new/master/apiSpec/new?filename=Keras_Applications_DenseNet_DenseNet201__ctor_System_Int32_System_Boolean_System_String_Numpy_NDarray_Keras_Shape_System_String_System_Int32_.md&amp;value=---%0Auid%3A%20Keras.Applications.DenseNet.DenseNet201.%23ctor(System.Int32%2CSystem.Boolean%2CSystem.String%2CNumpy.NDarray%2CKeras.Shape%2CSystem.String%2CSystem.Int32)%0Asummary%3A%20'*You%20can%20override%20summary%20for%20the%20API%20here%20using%20*MARKDOWN*%20syntax'%0A---%0A%0A*Please%20type%20below%20more%20information%20about%20this%20API%3A*%0A%0A">Improve this Doc</a>
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    <a href="https://github.com/SciSharp/Keras.NET/blob/master/Keras/Applications/DenseNet.cs/#L129">View Source</a>
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  <a id="Keras_Applications_DenseNet_DenseNet201__ctor_" data-uid="Keras.Applications.DenseNet.DenseNet201.#ctor*"></a>
  <h4 id="Keras_Applications_DenseNet_DenseNet201__ctor_System_Int32_System_Boolean_System_String_Numpy_NDarray_Keras_Shape_System_String_System_Int32_" data-uid="Keras.Applications.DenseNet.DenseNet201.#ctor(System.Int32,System.Boolean,System.String,Numpy.NDarray,Keras.Shape,System.String,System.Int32)">DenseNet201(Int32, Boolean, String, NDarray, Shape, String, Int32)</h4>
  <div class="markdown level1 summary"><p>Initializes a new instance of the <a class="xref" href="Keras.Applications.DenseNet.DenseNet201.html">DenseNet201</a> class.</p>
</div>
  <div class="markdown level1 conceptual"></div>
  <h5 class="decalaration">Declaration</h5>
  <div class="codewrapper">
    <pre><code class="lang-csharp hljs">public DenseNet201(int blocks, bool include_top = true, string weights = &quot;imagenet&quot;, NDarray input_tensor = null, Shape input_shape = null, string pooling = &quot;None&quot;, int classes = 1000)</code></pre>
  </div>
  <h5 class="parameters">Parameters</h5>
  <table class="table table-bordered table-striped table-condensed">
    <thead>
      <tr>
        <th>Type</th>
        <th>Name</th>
        <th>Description</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td><span class="xref">System.Int32</span></td>
        <td><span class="parametername">blocks</span></td>
        <td><p>numbers of building blocks for the four dense layers.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Boolean</span></td>
        <td><span class="parametername">include_top</span></td>
        <td><p>optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format) for NASNetMobile or (331, 331, 3) (with 'channels_last' data format) or (3, 331, 331) (with 'channels_first' data format) for NASNetLarge. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.String</span></td>
        <td><span class="parametername">weights</span></td>
        <td><p>one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">Numpy.NDarray</span></td>
        <td><span class="parametername">input_tensor</span></td>
        <td><p>optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.</p>
</td>
      </tr>
      <tr>
        <td><a class="xref" href="Keras.Shape.html">Shape</a></td>
        <td><span class="parametername">input_shape</span></td>
        <td><p>optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with 'channels_last' data format) or (3, 224, 224) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.String</span></td>
        <td><span class="parametername">pooling</span></td>
        <td><p>optional pooling mode for feature extraction when include_top is False.
None means that the output of the model will be the 4D tensor output of the last convolutional layer.
avg means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor.
max means that global max pooling will be applied.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Int32</span></td>
        <td><span class="parametername">classes</span></td>
        <td><p>optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.</p>
</td>
      </tr>
    </tbody>
  </table>
  <h3 id="implements">Implements</h3>
  <div>
      <span class="xref">System.IDisposable</span>
  </div>
</article>
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